Scene text recognition has attracted particular research interest because it
is a very challenging problem and has various applications. The most
cutting-edge methods are attentional encoder-decoder frameworks that learn the
alignment between the input image and output sequences. In particular, the
decoder recurrently outputs predictions, using the prediction of the previous
step as a guidance for every time step. In this study, we point out that the
inappropriate use of previous predictions in existing attention mechanisms
restricts the recognition performance and brings instability. To handle this
problem, we propose a novel module, namely adaptive embedding gate(AEG). The
proposed AEG focuses on introducing high-order character language models to
attention mechanism by controlling the information transmission between
adjacent characters. AEG is a flexible module and can be easily integrated into
the state-of-the-art attentional methods. We evaluate its effectiveness as well
as robustness on a number of standard benchmarks, including the IIIT$5$K, SVT,
SVT-P, CUTE$80$, and ICDAR datasets. Experimental results demonstrate that AEG
can significantly boost recognition performance and bring better robustness.

本研究提出了一种新的模块，即自适应嵌入门 (AEG)，并将其引入到注意机制中以解决预测限制对于文字场景识别表现的影响。该模块可以显著提高识别表现并带来更好的稳健性。

基于注意力机制的情境文本识别自适应嵌入门

Adaptive Embedding Gate for Attention-Based Scene Text Recognition

We incorporate morphological supervision into character language models
(CLMs) via multitasking and show that this addition improves bits-per-character
(BPC) performance across 24 languages, even when the morphology data and
language modeling data are disjoint. Analyzing the CLMs shows that inflected
words benefit more from explicitly modeling morphology than uninflected words,
and that morphological supervision improves performance even as the amount of
language modeling data grows. We then transfer morphological supervision across
languages to improve language modeling performance in the low-resource setting.

本研究通过多任务学习将形态学监督引入字符语言模型，并发现即使形态学数据和语言建模数据不重叠，这种增加也可以提高 24 种语言的 BPC 性能。同时，研究表明有屈折词的性能获益更大，而形态学督导即使在语言建模数据增加的情况下也能提高性能。最后，我们通过跨语言转移形态学督导来提高低资源环境下的语言建模性能。